High resolution of an image refers to that pixels contained by the image have high density, which can provide abundant detailed information, and can describe an objective scene more accurate and meticulous. A high-resolution image is widely desired in an information age, and is significantly applied in fields such as satellite remote sensing images, video security monitoring, military detection aerial photography, medical digital imaging, and video standard conversion.
Current image super-resolution technologies are mainly divided into two categories: a reconstruction-based super-resolution technology and a learning-based super-resolution technology. In the reconstruction-based super-resolution technology, all the information can only be obtained from input image data, without any additional background knowledge, and the whole resolution process is equivalent to an information extraction and information fusion process. As a resolution amplification factor increases, a quantity of input image samples that need to be provided increases dramatically; however, after an upper limit of the amplification factor is reached, no matter how many input image samples are added, it is impossible to improve a reconstruction effect.
In view of limitation of a reconstruction algorithm, the learning-based super-resolution technology has emerged as a frontier research field. The method generates a learning model by using an image training set, and creates details of the image with high occurring frequencies by using the model. The learning-based super-resolution method greatly improves image quality, but it also has some deficiencies, that is, a local image database collects currently existing video images as training samples, which have already been fixed and are unchangeable, and so there is a certain degree of limitation to improve the effect of the image quality.
Therefore, there is a need of a super-resolution technology which can further improve the image quality.